Bayesian decision theory

Abstract

Recommender systems assist users in finding relevant enti- ties according to their individual preferences. The entities’ properties along with their relationships must be considered in order to articulate good recommendations. In this pa- per, we present an approach for developing an adaptive hy- brid recommender system with semantic data. Such data is represented as large graph of nodes (semantic entities) and edges (semantic relations) filled with contents collected from Linked-Open-Data sources. The system implements dif- ferent algorithms to generate recommendations supporting users in finding relevant, but potentially unknown movies. The system provides users with explicit explanations helping them to understand why a movie is relevant. Users may re- fine requests according to their individual preferences. The system considers run-time complexity to guarantee a short request response time for individually adapted requests.